RUTICO: Recommending Successful Learning Paths Under Time Constraints

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Nabizadeh, AH (Author) Other; Jorge, AM (Author) FCUP; José Paulo Leal (Author) FCUP

Conference proceedings International

Title: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP 2017, Bratislava, Slovakia, July 09 - 12, 2017

Pages: 153-158

Indexing

Scopus - 21 Citations

Other information

Authenticus ID: P-00N-9QJ

DOI: 10.1145/3099023.3099035

Abstract (EN): Nowadays using E-learning platforms such as Intelligent Tutoring Systems (ITS) that support users to learn subjects are quite common. Despite the availability and the advantages of these systems, they ignore the learners' time limitation for learning a subject. In this paper we propose RUTICO, that recommends successful learning paths with respect to a learner's knowledge background and under a time constraint. RUTICO, which is an example of Long Term goal Recommender Systems (LTRS), a.er locating a learner in the course graph, it utilizes a Depth-first search (DFS) algorithm to find all possible paths for a learner given a time restriction. RUTICO also estimates learning time and score for the paths and finally, it recommends a path with the maximum score that satisfies the learner time restriction. In order to evaluate the ability of RUTICO in estimating time and score for paths, we used the Mean Absolute Error and Error. Our results show that we are able to generate a learning path that maximizes a learner's score under a time restriction. © 2017 ACM.

Language: English

Type (Professor's evaluation): Scientific